Enterprise AI adoption has reached an inflection point. According to McKinsey's State of AI 2025 survey, 88% of organizations now use AI in at least one business function. But the build-versus-buy calculus has shifted dramatically.
Total corporate AI investment reached $252.3 billion in 2024 (44.5% year-over-year growth), with enterprise generative AI spending hitting $13.8 billion—a 6x increase from the prior year. Gartner projects worldwide AI spending will reach $1.5 trillion in 2025.
Total Cost of Ownership Analysis
Building AI Solutions
3-Year TCO: ~$5.4M
- Year 1: $2.0M
- Years 2-3: $1.7M/year
- Ongoing maintenance: 35% of initial costs annually
Buying AI Solutions
3-Year TCO: ~$1.82M
- Year 1: $825K
- Years 2-3: $495K/year
- Roughly one-third the cost of building
Building AI: Cost Structure
Talent costs represent the largest component:
| Role | Annual Salary (US) |
|---|---|
| Machine Learning Engineer | $155,000-$260,000 |
| Data Scientist | $129,500-$185,700 |
| MLOps Engineer | $152,000-$185,800 |
AI talent commands a 30-50% premium over traditional IT roles. Average enterprise AI projects require 7-10 specialized roles.
Infrastructure costs add substantially: cloud GPU compute runs $10,000-$50,000+/month, on-premises H100 GPUs cost ~$30,000 per unit plus 20-40% for power/cooling/maintenance.
Timeline: Build typically spans 12-24 months to production.
Buying AI: Cost Structure
| Use Case | Annual Cost |
|---|---|
| Customer Support AI (Zendesk/Intercom) | $150K-$400K |
| Sales Intelligence (Gong/Clari) | $200K-$600K |
| Supply Chain AI | $500K-$2M |
| Enterprise Copilots | $30-$50/user/month |
Implementation costs range from $50K-$150K for basic deployment to $500K-$2M for complex legacy system integration.
Hidden Costs Often Overlooked
- Technical debt compounds at ~7% annually (postponing upgrades increases costs by up to 600%)
- Talent churn costs 50-60% of annual salary per departure
- Opportunity cost of delayed time-to-market
- Vendor lock-in: switching costs typically 2x initial investment
- Integration complexity: 95% of IT leaders report integration hurdles
- Data egress fees and dependency on vendor roadmap
Decision Framework
Build When:
- AI is core to competitive advantage (recommendation engines, pricing optimization)
- Unique workflows or proprietary data that generic solutions can't accommodate
- Deep integration with proprietary systems where building is faster than forcing fit
- Scale economics favor build (thousands of users, millions of transactions)
- Data sensitivity requires complete control (PHI, PII, financial data)
Buy When:
- Limited AI/ML talent available—can't compete for top-tier engineering
- Non-core business functions (HR, finance, IT operations)
- Commodity use cases (note-taking, Q&A, ticket deflection, basic code copilots)
- Testing/experimentation phase—validate before building v2
- Speed-to-value determines success
Hybrid Approach (Recommended for Most)
63% of consulting clients achieve optimal results with hybrid approaches using vendor platforms for governance/compliance while building custom "last mile" capabilities.
Case Studies: Companies That Built Successfully
Walmart Supply Chain AI
Custom truck routing and load optimization system won the INFORMS Franz Edelman Award. Results: $75 million annual savings with 72 million pounds CO₂ reduction. Rationale: core competitive advantage with unique logistics data.
McKinsey's Lilli Platform
Internal GenAI platform launched July 2023. 72% of 45,000 employees use it with 500K+ monthly prompts, achieving 30% reduction in research time. Rationale: proprietary knowledge base and confidential client data.
LinkedIn EON Models
Custom Llama-based models trained on 200M tokens from their Economic Graph. Results: 75x cheaper than GPT-4 and 30% more accurate than base Llama-3 for their use cases.
Analyst Guidance
McKinsey recommends evaluating: strategy alignment, cost analysis, tech requirements, time-to-market, risk assessment, and capability building. Key finding: "Capturing full value requires rethinking how companies operate—not just accelerating what they already do."
Forrester warns that 67% of software projects fail due to wrong build-versus-buy choices, emphasizing that structured decision frameworks yield 25-35% better outcomes.
Key Takeaways
- Default to buy for non-differentiating capabilities
- Build only when AI creates defensible competitive advantage
- Plan for hybrid—most successful enterprises use both approaches
- Factor in time-to-value: Buy delivers in 3-9 months vs 12-24 months for build
- Include hidden costs in TCO analysis (talent churn, technical debt, vendor lock-in)